Hongling Chen

Title: Innovations of Hongling Chen in Seismic Inversion Technology

Introduction

Hongling Chen is a prominent inventor based in Xi'an, China. She has made significant contributions to the field of seismic inversion technology. Her innovative approach combines model-driven optimization with data-driven deep learning methods, resulting in more reliable inversion outcomes.

Latest Patents

Hongling Chen holds a patent for a "Model-driven deep learning-based seismic super-resolution inversion method." This method includes several key steps: first, mapping each iteration of a model-driven alternating direction method of multipliers (ADMM) into each layer of a deep network. Second, it involves learning proximal operators through a data-driven method to construct the deep network ADMM-SRINet. Third, the method obtains label data to train the deep network. Finally, it inverts test data using the trained deep network ADMM-SRINet. This innovative approach leverages the strengths of both model-driven optimization and data-driven deep learning, enhancing the interpretability of the network while reducing the training set requirements.

Career Highlights

Hongling Chen is affiliated with Xi'an Jiaotong University, where she continues to advance her research in seismic inversion technology. Her work has garnered attention for its practical applications in geophysical exploration and resource management.

Collaborations

Hongling Chen collaborates with notable colleagues, including Jinghuai Gao and Zhaoqi Gao. Their combined expertise contributes to the advancement of innovative solutions in their field.

Conclusion

Hongling Chen's contributions to seismic inversion technology exemplify the intersection of model-driven and data-driven methodologies. Her innovative patent reflects a significant advancement in the field, promising more reliable results in seismic data interpretation.